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CorefDRE: Coref-Aware Document-Level Relation Extraction

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Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13370))

Abstract

Document-level Relation Extraction (Doc-level RE) aims to extract relations among entities from a document, which requires reasoning over multiple sentences. The pronouns are ubiquitous in the document, which can provide reasoning clues for Doc-level RE. However, previous works do not take the pronouns into account. In this paper, we propose Coref-aware Doc-level RE based on Graph Inference Network (CorefDRE) to infer relations. CorefDRE first dynamically constructs the heterogeneous Mention-Pronoun Affinity Graph (MPAG) by integrating coreference information of pronouns. Then, Entity Graph (EG) is aggregated from MPAG through the weight of mention-pronoun pairs, calculated by the noise suppression mechanism, and GCN. Finally, we infer relations between entities based the normalized EG. Moreover, We introduce the noise suppression mechanism via calculating affinity between pronouns and corresponding mentions to filter the noise caused by pronouns. Experimental results significantly outperform baselines by nearly 1.7–2.0 in F1 on three public datasets, DocRED, DialogRE, and MPDD. We further conduct ablation experiments to demonstrate the effectiveness of the proposed MPAG structure and the noise suppression mechanism.

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Acknowledgements

The authors would like to thank the Associate Editor and anonymous reviewers for their valuable comments and suggestions. This work is funded in part by the National Natural Science Foundation of China under Grants No.62176029, and in part by the graduate research and innovation foundation of Chongqing, China under Grants No. CYB21063. This work also is supported in part by the National Key Research, Development Program of China under Grants 2017YFB1402400, Major Project of Chongqing Higher Education Teaching Reform Research (191003), and the New Engineering Research and Practice Project of the Ministry of Education (E-JSJRJ20201335).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Zhongxuan Xue, Jiang Zhong, Qizhu Dai & Rongzhen Li

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  1. Zhongxuan Xue
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  2. Jiang Zhong
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  3. Qizhu Dai
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  4. Rongzhen Li
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Corresponding author

Correspondence to Jiang Zhong .

Editor information

Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Xue, Z., Zhong, J., Dai, Q., Li, R. (2022). CorefDRE: Coref-Aware Document-Level Relation Extraction. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-10989-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10988-1

  • Online ISBN: 978-3-031-10989-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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